Drop irrigation head intelligent decision and management method based on Internet of Things

Abstract

The invention discloses a drip irrigation head intelligent decision and management method based on the Internet of Things, and particularly relates to the technical field of agricultural irrigation. The three parameters are comprehensively analyzed to obtain a filtering hidden danger index, when the filtering hidden danger index is higher than a preset threshold value, a backwashing instruction is triggered, the blocking degree, efficiency attenuation and accumulated load are comprehensively reflected, misjudgment of a single index is avoided, and the problem that in the prior art, a filter backwashing control strategy is often based on a single index analysis result, and the filtering efficiency is low is solved. And misjudgment or neglecting of actual working condition difference due to instantaneous fluctuation is easy to cause poor accuracy.

Description

Technical Field

[0001] This invention relates to the field of agricultural irrigation technology, and more specifically, to an intelligent decision-making and management method for drip irrigation heads based on the Internet of Things. Background Technology

[0002] With the penetration of emerging technologies such as the Internet of Things, big data, and sensors into the agricultural field, a glimmer of hope has been brought for the refined management of agricultural production.

[0003] However, existing intelligent decision-making and management methods for drip irrigation heads still have the following shortcomings in practical applications: The control strategy for filter backwashing is often based on the analysis results of a single index, which is prone to misjudgment due to instantaneous fluctuations or neglect of actual operating conditions, resulting in poor accuracy. In addition, fertilizer application is mostly based on traditional fertilization methods that rely on manual experience, without taking into account the nutrient content of crops at different growth stages. This results in either excessive or insufficient use of fertilizer. Excessive fertilization not only increases production costs but also leads to low fertilizer utilization, while insufficient fertilization severely restricts crop growth, resulting in reduced crop yield and quality.

[0004] To address this, an IoT-based intelligent decision-making and management method for drip irrigation heads has been developed. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent decision-making and management method for drip irrigation heads based on the Internet of Things.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A smart decision-making and management method for drip irrigation heads based on the Internet of Things, comprising: IoT monitoring: Real-time collection of drip irrigation head unit operation data and crop data, including operation data such as sedimentation tank water level, head unit water output, and filter operation data; crop data such as crop growth stage, crop type, growth data, and soil nutrients; Data analysis: The operation data of the drip irrigation head and crop data within the current set time window are analyzed and processed to obtain the initial opening degree of the water inlet gate, the filter backwashing command and the fertilization plan of the drip irrigation head in the next set time window, and integrated into a decision-making scheme; Intelligent control; based on the generated decision scheme, the water inlet gate of the sedimentation tank of the irrigation head, the amount of fertilizer, the number of times of fertilization, and the backwashing of the filter are managed and adjusted.

[0007] Specifically, the analysis of the grit chamber water level in the drip irrigation head unit operation data includes: Real-time data collection of water level in the sedimentation tank to construct a water level sequence within a set time window. Where n is the total number of time points; The average value of each group of water level data in the sequence is calculated to obtain the water level performance height Z of the sedimentation tank within the current set time window. A preset safe height range is defined, and the calculated water level performance height is compared with this safe height range. If the water level performance height is not within the safe height range, a comparison expression is used. Determine the abnormal water level value Li based on the water level indication height; where i = 1 or 2; Z and Z represent the safe height range and the water level performance height, respectively. These represent the highest and lowest values ​​within the safe height range, respectively. Each high-level interval and low-level interval corresponding to the water level anomaly value Li is pre-constructed, and each high-level interval or low-level interval corresponds to a set of opening adjustment ratios. Based on the comparison results, if i=1, the water level anomaly value L1 is input into each high-level interval for matching; if i=2, the water level anomaly value L2 is input into each low-level interval for matching. The resulting opening adjustment ratio is recorded as P1.

[0008] Specifically, the analysis of the headwater discharge in the drip irrigation headwater operation data includes: For the instantaneous flow rate data of the headwater discharge at each time point within the set time window, calculate the average instantaneous flow rate within the set time window, and calculate the ratio of the average instantaneous flow rate as the numerator and the preset benchmark flow rate as the denominator to obtain the discharge rate evaluation value. Divide the time window into a front time zone and a back time zone by the midpoint of the set time window. Calculate the average value of the instantaneous flow data in both the front and back time zones to obtain the front time average and the back time average. Calculate the ratio between the back time average as the numerator and the front time average as the denominator to obtain the trend correction coefficient. The corrected water output value is obtained by multiplying the water output assessment value by the trend correction coefficient. Each set of water output value intervals corresponding to the corrected water output value is pre-constructed, and each set of water output value intervals corresponds to a set of opening adjustment ratios. The calculated corrected water output value is matched with each set of water output value intervals, and the resulting opening adjustment ratio is recorded as P2.

[0009] Specifically, obtaining the initial opening degree of the inlet gate of the drip irrigation head in the next set time window is as follows: The intake gate opening optimization ratio P3 is obtained by combining P1 and P2. If P3 is negative, it means that the intake gate opening is reduced; otherwise, it means that the intake gate opening is increased. The value of P3 is the specific reduction or increase ratio. The intake gate opening adjusted by the intake gate opening optimization ratio P3 is used as the initial intake gate opening for the next set time window.

[0010] Specifically, the analysis of filter operation data in the drip irrigation head unit operation data includes: The pressure difference between the inlet and outlet of the filter is obtained at each time point within a set time window, and the average value is calculated to obtain the pressure difference evaluation value of the filter, which is represented by kt. The instantaneous flow rates at the inlet and outlet of the filter are obtained at each time point within a set time window. After averaging these values, the average inlet flow and average outlet flow are obtained, denoted as m1 and m2, respectively. The flow rate attenuation rate, denoted as kr, is calculated using the formula (m1-m2) / m1×100%. The duration of continuous operation of the identification filter since the last backwash is recorded as the duration, denoted by kc; according to the formula... Calculate the filtration hazard index of the filter within the set time window. ;in These represent the differential pressure threshold, the flow rate attenuation threshold, and the theoretical periodic backwashing duration, respectively. These represent the weighting coefficients corresponding to the differential pressure assessment value, flow rate attenuation rate, and duration, respectively; the filtration hazard index... The filter is compared with a preset hazard threshold index. If the value is higher than the hazard threshold index, a filter backwashing command is triggered.

[0011] Specifically, the analysis of crop data in the drip irrigation head unit operation data includes: Extract the crop type and growth stage of the currently planted crop; preset the theoretical plant height and theoretical leaf area for different growth stages corresponding to the currently planted crop; randomly collect the plant height and leaf area of ​​group e of the currently planted crops, and calculate the average value to obtain the actual plant height and actual leaf area of ​​the regional crops, where e > 5; use the actual plant height and actual leaf area as the numerator and the theoretical plant height and theoretical leaf area as the denominator, respectively, and calculate the ratio between each pair to obtain the plant height performance value and leaf surface performance value of the regional crops; multiply the plant height performance value and leaf surface performance value of the regional crops by the preset plant height weight coefficient and leaf surface weight coefficient, respectively, and then sum them to obtain the growth evaluation value of the regional crops, denoted as Lr; Randomly collect soil nutrient content in the irrigation area corresponding to the crop in the current region for group e. The type number of soil nutrient content is represented by n, where n=1,2,...,m, and m is the total number of soil nutrient content types measured. Calculate the average value of soil nutrient content for each group of the same type to obtain the content assessment value Gn of each type of soil nutrient corresponding to the crop in the current region. Preset the theoretical soil nutrient requirement threshold Vn for different growth stages of the currently planted crop; use the formula Calculate the nutrient content status value Lc of crops in the current area; The weighting coefficient represents the weighting coefficient of the content assessment value Gn corresponding to various types of soil nutrients.

[0012] Specifically, obtaining the fertilization plan for the drip irrigation head in the next set time window is as follows: The growth assessment value Lr of the regional crop is compared with the set normal range of assessment values. If the growth assessment value Lr is higher than the normal range of assessment values, the regional crop is determined to be in an over-growing state. If the growth assessment value Lr is lower than the normal range of assessment values, the regional crop is determined to be in a lag state of growth. If the growth assessment value Lr is within the normal range of assessment values, the regional crop is determined to be in a normal growth state. The nutrient content status value Lc of the current crop in the region is compared with the set normal range. If the nutrient content status value Lc is higher than the normal range, the crop in the region is judged to be in a state of nutrient excess. If the nutrient content status value Lc is lower than the normal range, the crop in the region is judged to be in a state of nutrient lag. If the nutrient content status value Lc is within the normal range, the crop in the region is judged to be in a state of normal nutrition. The states of excessive growth, delayed growth, normal growth, excessive nutrition, delayed nutrition, and normal nutrition are labeled as S1, S2, S3, U1, U2, and U3, respectively. If the current crop status combination in the region is as follows Then, a fertilizer reduction strategy is generated, which includes reducing the number of fertilizations and reducing the proportion of fertilizer applied. If the current crop status combination in the region is as follows Then, a fertilization increase strategy is generated, where the fertilization decrease strategy includes increasing the number of fertilizations and increasing the proportion of fertilization.

[0013] Specifically, obtaining the fertilization plan for the drip irrigation head within the next set time window also includes: If the current crop status combination in the region is as follows Then, an exception is generated to formulate a strategy; Extract historical cases from the database that correspond to the state combinations used in the anomaly-based policy formulation. Each historical case includes the historical state combination performance, historical fertilization strategy, type of crop fertilized, and growth stage of the crop fertilized. Extract the growth assessment value and nutrient content status value of each historical case and correlate them with the current regional crop growth assessment value Lr and nutrient content status value Lc using a formula. Calculate the available confidence index Where La and Lf represent the growth assessment value and nutrient content status value of each group of historical cases, respectively, and b1 and b2 are the weighting coefficients corresponding to the growth assessment value Lr and the nutrient content status value Lc; select the available confidence index. The lowest historical cases were identified, and historical fertilization strategies were extracted as replacement strategies for current regional crops. The generated fertilizer reduction strategy, fertilizer increase strategy, or fertilizer replacement strategy will be used as the fertilization plan for the crop in the current region for the next set time window.

[0014] The technical effects and advantages of this invention are as follows: (1) By collecting the pressure difference between the filter inlet and outlet, the inlet and outlet flow rates, and the continuous running time since the last backwash, the three parameters are comprehensively analyzed to obtain the filter hazard index. When the filter hazard index is higher than the preset threshold, the backwash command is triggered. This comprehensively reflects the degree of blockage, efficiency decay and cumulative load, avoids misjudgment of a single indicator, and solves the problem that the control strategy for filter backwashing in the existing technology is often based on the analysis results of a single indicator, which is prone to misjudgment due to instantaneous fluctuations or ignores the differences in actual working conditions, resulting in poor accuracy. (2) By calculating crop growth assessment values ​​and soil nutrient status values ​​and matching them with the set normal range, the growth status and nutrient status of the crop are determined. The corresponding fertilization strategy is generated based on the combination of growth status and nutrient status. The amount and frequency of fertilization are dynamically adjusted based on the fertilization strategy. At the same time, the occurrence of abnormal combinations is considered. When abnormal combinations occur, the optimal fertilization replacement strategy is matched through the historical case database. This improves the intelligence and accuracy of fertilization and solves the problem that the application of fertilizer in the existing technology is mostly based on human experience in the traditional fertilization mode, which does not take into account the nutrient content of the crop at each growth stage. (3) By integrating the abnormal water level of the sedimentation tank with the trend of the headwater discharge, the opening optimization ratio is accurately calculated with the dual-dimensional adjustment ratio. This ensures that the water level is stable within the safe range to avoid overflow or insufficient water supply. It also achieves dynamic balance between the inlet and outlet speeds by correcting the outlet discharge trend. This eliminates the reliance on traditional manual experience and improves the accuracy, timeliness and adaptability of the headwater inlet gate adjustment in drip irrigation, ensuring the efficient and stable operation of the entire drip irrigation system. Attached Figure Description

[0015] Figure 1 This is a flowchart of an IoT-based intelligent decision-making and management method for drip irrigation heads according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example

[0018] like Figure 1 As shown, an IoT-based intelligent decision-making and management method for drip irrigation heads is as follows: IoT monitoring: Real-time collection of drip irrigation head operation data and crop data, and integration into data packages; The operational data includes the sedimentation tank water level, the head outlet flow rate, and the filter operation data; the crop data includes the crop growth stage, crop type, growth data, and soil nutrients. Water level data in the sedimentation tank is collected by a water level sensor. The flow rate and pressure data of the head outlet are collected by a flow meter and a pressure sensor. The operating status of the filter is monitored by sensors to obtain information such as filter clogging and operating time. Crop growth data, such as crop height, leaf condition, and soil nutrient content, are collected through image acquisition devices and soil sensors. Data transmission: The collected operational and crop data are transmitted to the data analysis module; the collected data is then transmitted encrypted via a 5G wireless network to the park's data center server for storage, ensuring the integrity and security of the data during transmission. Data analysis: The operation data of the drip irrigation head and crop data within the current set time window are analyzed and processed to obtain the initial opening degree of the water inlet gate, the filter backwashing command and the fertilization plan of the drip irrigation head in the next set time window, and integrated into a decision-making scheme; The specific evaluation process for the opening degree of the intake gate is as follows: Real-time data collection of water level in the sedimentation tank to construct a water level sequence within a set time window. Where n is the total number of time points; sampling frequency: 10 minutes / time; The average value of each group of water level data in the sequence is calculated to obtain the water level performance height Z of the grit chamber within the current set time window. A preset safe water level range (i.e., between the highest and lowest safe water levels) is then defined. The calculated water level performance height is compared with this safe range. If the water level performance height is not within the safe range, a comparison expression is used. Determine the abnormal water level value Li based on the water level indication height; where i = 1 or 2; Z and Z represent the safe height range and the water level performance height, respectively. These represent the highest and lowest values ​​within the safe height range, respectively. When the water level is below the safe height range, it indicates that the current water intake is insufficient, which may lead to unstable water output at the head (such as air intake of the water pump or a sudden drop in water supply pressure). In this case, it is necessary to increase the opening of the water inlet gate to increase the water intake and raise the water level until it is restored to the safe range. When the water level is above the safe height range: there is a risk of overflow, which may lead to problems such as overloading the sedimentation tank and backflow of sediment. In this case, it is necessary to reduce the opening of the inlet gate (or even close it) to reduce the inflow and lower the water level to avoid exceeding the safe range; Each high-level interval and low-level interval corresponding to the water level anomaly value Li is pre-constructed, and each high-level interval or low-level interval corresponds to a set of opening adjustment ratios. Based on the comparison results, if i=1, the water level anomaly value L1 is input into each high-level interval for matching; if i=2, the water level anomaly value L2 is input into each low-level interval for matching. The resulting opening adjustment ratio is recorded as P1. The opening adjustment ratio corresponding to each low-level interval is negative, and the higher the water level anomaly value L2, the higher the corresponding opening adjustment ratio. Conversely, the opening adjustment ratio corresponding to each high-level interval is positive, and the higher the water level anomaly value L1, the higher the corresponding opening adjustment ratio. Example: High-level range 1: 0-0.2 → Opening adjustment ratio = +5%; High-level range 2: 0.2-0.5 → Opening adjustment ratio = +10%; High-level range 3: >0.5 → Opening adjustment ratio = +15%; Low range 1: 0-0.2 → Opening adjustment ratio = -5%; Low range 2: 0.2-0.5 → Opening adjustment ratio = -10%; Low range 3: >0.5 → Opening adjustment ratio = -15%; For the instantaneous flow rate data of the headwater discharge at each time point within the set time window, the average instantaneous flow rate within the set time window is calculated. The average instantaneous flow rate is used as the numerator and the preset benchmark flow rate is used as the denominator to calculate the ratio and obtain the discharge rate assessment value. The benchmark flow rate is calculated through historical operating data to determine the range of headwater discharge that the grit chamber can stably guarantee. The median or optimal value of this range is taken as the benchmark flow rate. The outflow rate of the first stage directly reflects the "outflow velocity" of the grit chamber, and it needs to maintain a dynamic balance with the "inflow velocity" of the inlet gate. When the estimated outflow rate is large: the outflow rate of the grit chamber increases. If the inflow rate remains unchanged, the water level will drop rapidly. In this case, it is necessary to increase the opening of the inflow gate to increase the inflow rate, offset the downward trend of the water level caused by the increased outflow rate, and maintain the water level stability. When the estimated outflow rate is low: the outflow rate of the grit chamber slows down. If the inflow rate remains unchanged, the water level will gradually rise. In this case, the opening of the inlet gate should be reduced to decrease the inflow rate and prevent the water level from exceeding the safe limit due to the reduced outflow rate. Divide the time window into a front time zone and a back time zone by the midpoint of the set time window. Calculate the average value of the instantaneous flow data in both the front and back time zones to obtain the front time average and the back time average. Calculate the ratio between the back time average as the numerator and the front time average as the denominator to obtain the trend correction coefficient. If the trend correction coefficient is greater than 1, it means that the instantaneous flow data in the later time zone generally shows an upward trend compared to the instantaneous flow data in the earlier time zone; if it is less than 1, the opposite is true. The water output assessment value is multiplied by the trend correction coefficient to obtain the corrected water output value. Each set of water output value intervals corresponding to the corrected water output value is pre-constructed, and each set of water output value intervals corresponds to a set of opening adjustment ratios. The higher the water output assessment value, the higher the probability that the corresponding opening adjustment ratio will be positive, and the larger the ratio. The lower the water output assessment value, the higher the probability that the corresponding opening adjustment ratio will be negative, and the larger the ratio. Example: Outflow value range 1: <0.8 → Opening adjustment ratio = -15%; Outflow value range 2: 0.8-1.2 → Opening adjustment ratio = 0%; Outflow value range 3: >1.2 → Opening adjustment ratio = +15%; The calculated corrected effluent value is matched with the effluent value range of each group, and the resulting opening adjustment ratio is recorded as P2. The intake gate opening optimization ratio P3 is obtained by combining P1 and P2. If P3 is negative, it means that the intake gate opening is reduced; otherwise, it means that the intake gate opening is increased. The value of P3 is the specific reduction or increase ratio. The intake gate opening adjusted by the intake gate opening optimization ratio P3 is used as the initial opening of the intake gate in the next set time window. If the ratio of the intake gate opening optimization ratio is greater than 5%, then 5% is used to limit the opening adjustment. The specific evaluation process for filter flushing is as follows: The pressure difference between the inlet and outlet of the filter is obtained at each time point within a set time window, and the average value is calculated to obtain the pressure difference evaluation value of the filter, which is represented by kt. When the filter is not clogged, the resistance to water flow through the filter media / screen is small, and the pressure difference is small; as impurities accumulate, the resistance increases, and the pressure difference gradually rises. The instantaneous flow rates at the inlet and outlet of the filter are obtained at each time point within a set time window. After averaging these values, the average inlet flow and average outlet flow are obtained, denoted as m1 and m2, respectively. The flow rate attenuation rate, denoted as kr, is calculated using the formula (m1-m2) / m1×100%. The duration of continuous operation of the identification filter since the last backwash is recorded as the duration, denoted by kc; according to the formula... Calculate the filtration hazard index of the filter within the set time window. ;in These represent the differential pressure threshold, the flow rate attenuation threshold, and the theoretical periodic backwashing duration, respectively. These represent the weighting coefficients corresponding to the differential pressure assessment value, flow rate attenuation rate, and duration, respectively. Filter the hazard index The filter is compared with the preset hazard threshold index. If the value is higher than the hazard threshold index, the filter backwashing command is triggered. The "Filter Hazard Index" is calculated by combining three core indicators: differential pressure assessment value, flow rate attenuation rate, and duration, with their respective weighting coefficients. This index comprehensively reflects the degree of filter clogging, the trend of filtration efficiency attenuation, and the cumulative operating load, avoiding the one-sidedness of a single indicator and making the judgment more consistent with the actual operating conditions. The specific evaluation process for the fertilization plan is as follows: Extract the crop type (such as vegetables, fruit trees, field crops, etc.) and growth stage (such as sowing period, seedling period, flowering period, fruiting period, maturity period, etc.) of the currently planted crop. The theoretical plant height and theoretical leaf area are preset for different growth stages of the current crop. For example, the theoretical plant height of wheat during the jointing stage is 60-70cm and the LAI is 3.0-3.5; the theoretical plant height of tomato during the flowering and fruit setting stage is 80-90cm and the LAI is 2.5-3.0. The preset values ​​are the midpoint of each range. Randomly collect the plant height and leaf area of ​​the currently planted crops in group e, and calculate the average value to obtain the actual plant height and actual leaf area of ​​the regional crops. Here, e>5, which is set by the technicians. This data serves as representative crop data for the current irrigation area. The actual plant height and actual leaf area are used as the numerators, and the theoretical plant height and theoretical leaf area are used as the denominators. The ratios between each pair are used to obtain the plant height performance value and leaf area performance value of the regional crops. The plant height performance value and leaf performance value of the regional crop are multiplied by the preset plant height weight coefficient and leaf weight coefficient respectively, and then summed to obtain the growth evaluation value of the regional crop, denoted as Lr. The growth assessment value Lr of the regional crop is compared with the set normal range of assessment values. If the growth assessment value Lr is higher than the normal range of assessment values, the regional crop is determined to be in an over-growing state. If the growth assessment value Lr is lower than the normal range of assessment values, the regional crop is determined to be in a lag state of growth. If the growth assessment value Lr is within the normal range of assessment values, the regional crop is determined to be in a normal growth state. The soil nutrient content of the irrigated area corresponding to the crop in the current region is randomly collected in group e. The type of soil nutrient content is represented by n, where n = 1, 2, ..., m, and m is the total number of soil nutrient content types measured. Soil nutrient content types include, but are not limited to, nitrogen, phosphorus, and potassium. The average value of the soil nutrient content of each group of the same type is calculated to obtain the content evaluation value Gn of each type of soil nutrient corresponding to the crop in the current region. The theoretical soil nutrient requirement threshold Vn corresponding to different growth stages of the currently planted crop is preset (e.g., wheat requires 80-100 mg / kg of available nitrogen and 30-40 mg / kg of phosphorus during the jointing stage). Using formula Calculate the nutrient content status value Lc of crops in the current area; The weighting coefficients represent the weighting coefficients of the content assessment values ​​Gn for various types of soil nutrients; The nutrient content status value Lc of the current crop in the region is compared with the set normal range. If the nutrient content status value Lc is higher than the normal range, the crop in the region is judged to be in a state of nutrient excess. If the nutrient content status value Lc is lower than the normal range, the crop in the region is judged to be in a state of nutrient lag. If the nutrient content status value Lc is within the normal range, the crop in the region is judged to be in a state of normal nutrition. Based on the crop growth status and combined with the actual soil nutrient supply capacity, the amount of fertilizer is dynamically adjusted to achieve on-demand replenishment and avoid waste or deficiency. The states of excessive growth, delayed growth, normal growth, excessive nutrition, delayed nutrition, and normal nutrition are labeled as S1, S2, S3, U1, U2, and U3, respectively. If the current crop status combination in the region is as follows Then, a fertilizer reduction strategy is generated, which includes reducing the number of fertilizations and reducing the proportion of fertilizer applied. If the current crop status combination in the region is as follows Then, a fertilization increase strategy is generated, where the fertilization decrease strategy includes increasing the number of fertilizations and increasing the proportion of fertilization. If the current crop status combination in the region is as follows Then, an exception is generated to formulate a strategy; Extract historical cases from the database that match the state combinations corresponding to the anomaly-defined strategy. Each historical case includes the historical state combination, historical fertilization strategy, fertilized crop type, and fertilized crop growth stage. In addition to matching the state combination, the extracted historical cases must also match the crop type and crop production stage. Extract the growth assessment value and nutrient content status value of each historical case and correlate them with the current region's crop growth assessment value Lr and nutrient content status value Lc using a formula. Calculate the available confidence index Where La and Lf represent the growth assessment value and nutrient content status value of each group of historical cases, respectively, and b1 and b2 are the weighting coefficients corresponding to the growth assessment value Lr and the nutrient content status value Lc; select the available confidence index. The lowest historical cases were identified, and historical fertilization strategies were extracted as replacement strategies for current regional crops. The generated fertilizer reduction strategy, fertilizer increase strategy, or fertilizer replacement strategy will be used as the fertilization plan for the crop in the current region for the next set time window. Intelligent control; based on the generated decision scheme, the water inlet gate of the sedimentation tank of the irrigation head, the amount of fertilizer, the number of times of fertilization, and the backwashing of the filter are managed and adjusted; In addition, the decision-making process for the drip irrigation head unit integrates key parameters such as the water level in the grit chamber / well, the head unit's outflow rate, and filter performance. The correlation between these parameters is reflected in: The water level in the sedimentation tank directly affects the stability of the headwater intake: excessively high water levels may cause a sudden increase in outlet pressure, increasing the filter load; excessively low water levels may cause the pump to draw in air, leading to fluctuations in outlet flow. By monitoring water level changes in real time (e.g., collecting data every 10 minutes) and combining this with trend analysis of the headwater outlet flow (comparing the average values ​​of the previous and subsequent time zones), the opening of the inlet gate is dynamically adjusted to ensure that the water level remains stable within a safe range (e.g., 2-3 meters) while matching the headwater outlet flow requirements (e.g., baseline flow ±10%). The filtration precision of a filter (e.g., a 120-mesh screen) determines the cleanliness of the water entering the field pipeline network, while the quality of backwash water (e.g., turbidity and impurity content) directly affects the filter's recovery effect. Decision-making must link filtration precision with the backwashing strategy: if high filtration precision is required (e.g., 80 mesh or higher for greenhouse crop drip irrigation), the backwashing trigger threshold needs to be lowered (e.g., triggered when the filtration hazard index > 0.6), and the backwashing duration extended (e.g., from 30 seconds to 60 seconds) to ensure thorough cleaning of the filter; if the backwash water source quality is poor (e.g., sand content > 5%), an additional 10 seconds of clean water rinsing should be added after backwashing to avoid secondary pollution. Taking into account factors such as water resources, equipment, and crop requirements, the head management system needs to achieve the following dynamic adjustments: When the water level in the sedimentation tank falls below the safety lower limit (e.g., <1.5 meters) or the well output decreases (e.g., 20% less than the baseline value), the system automatically triggers the water supply guarantee mode. Reduce the first outlet flow rate to 70% of the baseline value and extend the single-group irrigation time (e.g., from 40 minutes to 60 minutes). Irrigation of non-critical crops (such as ornamental plants) should be suspended, and priority should be given to staple crops (such as wheat and corn). If the water level continues to drop for more than 4 hours, an alert will be sent to management personnel, and it is recommended to activate the backup water source (such as a water storage tank).

[0019] Based on the initial outlet water pressure (e.g., set within the normal range of 0.2-0.3 MPa) and flow rate data, dynamically adjust the water pump power: When the outlet water pressure is greater than 0.35 MPa, reduce the water pump frequency (e.g., from 50 Hz to 40 Hz) to avoid pipe bursting. When the outflow rate is less than 80% of the reference value and the pressure is normal, it is determined that the water pump impeller is worn, the running time is automatically recorded and maintenance is prompted; By taking advantage of off-peak electricity usage times (such as 0-6 am), the water pump power is automatically increased to 110% of its rated value to speed up irrigation and reduce energy costs.

[0020] In addition to the conventional filtration hazard index calculation, filter status monitoring is added: if the turbidity of the effluent is greater than 5 NTU by a turbidity sensor (accuracy ±1 NTU) installed at the filter outlet, an emergency backwash will be triggered even if the filtration hazard index has not reached the threshold. For water sources with high sediment content (such as irrigation areas of the Yellow River), a vortex sedimentation device is added before the filter, and the sand discharge valve is automatically opened according to the sediment content of the influent (e.g., >10kg / m³) (sand discharge for 10 seconds every 30 minutes) to reduce the filter load.

[0021] Based on field soil moisture (e.g., 0-30cm soil moisture content < 60% field water holding capacity), the priority of rotation irrigation is automatically generated: the area with the lowest moisture content is given priority to be included in the next round of irrigation; When a valve in the field closes due to a malfunction (such as interruption of feedback signal), the head system immediately adjusts the outflow rate (such as reducing the design flow rate of the corresponding irrigation group) and marks the area where the malfunctioning valve is located as "to be repaired", skipping the area and entering the next irrigation group; When the water level rises (e.g., the sedimentation tank water level rises from 1.2 meters to 2.5 meters), the system automatically lifts the water supply restriction, restores the normal outflow, and shortens the irrigation interval (e.g., from 8 hours to 6 hours) to make up for the previous shortfall in irrigation. If the soil moisture is still not up to standard after a certain irrigation group in the field is completed (e.g., water content <70%), the head unit will automatically extend the irrigation time of that group (e.g., by 10 minutes) and postpone the start time of the next irrigation group accordingly to ensure irrigation effect; Through the above optimizations, the drip irrigation head system can achieve intelligent coordination across the entire chain from water source to field, ensuring safe and stable operation of equipment, accurately matching crop needs, improving irrigation efficiency by 15-20%, and reducing water waste by more than 30%. The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0022] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0023] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0024] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0025] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0026] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0027] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0028] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0029] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An Internet of Things-based intelligent decision-making and management method for drip irrigation heads, characterized in that, include: IoT monitoring: Real-time collection of drip irrigation head unit operation data and crop data, including operation data such as sedimentation tank water level, head unit water output, and filter operation data; crop data such as crop growth stage, crop type, growth data, and soil nutrients; Data analysis: The operation data of the drip irrigation head and crop data within the current set time window are analyzed and processed to obtain the initial opening degree of the water inlet gate, the filter backwashing command and the fertilization plan of the drip irrigation head in the next set time window, and integrated into a decision-making scheme; Intelligent control: Based on the generated decision scheme, the system manages and adjusts the inlet gate of the sedimentation tank of the irrigation head, the amount of fertilizer applied, the number of times fertilizer is applied, and the backwashing of the filter.

2. The method according to claim 1, wherein, The analysis of the sedimentation tank water level in the drip irrigation head unit operation data is as follows: Real-time acquisition of the water level height of the grit chamber water level, and construction of a water level height sequence within a defined time window ; wherein n is the total number of time points; The average value calculation is performed on each set of water level height data in the sequence to obtain the water level performance height Z of the grit chamber in the current set time window, the safety height interval corresponding to the preset water level height, the calculated water level performance height is compared with the safety height interval, if the water level performance height is not in the safety height interval, based on the comparison expression determines the water level abnormal value Li of the water level performance height; wherein i=1 or 2; and Z respectively represent the safety height interval and the water level performance height, respectively represent the maximum value and the minimum value in the safety height interval; Each high-level interval and low-level interval corresponding to the water level anomaly value Li is pre-constructed, and each high-level interval or low-level interval corresponds to a set of opening adjustment ratios. Based on the comparison results, if i=1, the water level anomaly value L1 is input into each high-level interval for matching; if i=2, the water level anomaly value L2 is input into each low-level interval for matching. The resulting opening adjustment ratio is recorded as P1.

3. The method according to claim 2, wherein, The analysis of the headwater discharge volume in the drip irrigation headwater operation data specifically includes: For the instantaneous flow rate data of the headwater discharge at each time point within the set time window, calculate the average instantaneous flow rate within the set time window, and calculate the ratio of the average instantaneous flow rate as the numerator and the preset benchmark flow rate as the denominator to obtain the discharge rate evaluation value. Divide the time window into a front time zone and a back time zone by the midpoint of the set time window. Calculate the average value of the instantaneous flow data in both the front and back time zones to obtain the front time average and the back time average. Calculate the ratio between the back time average as the numerator and the front time average as the denominator to obtain the trend correction coefficient. The corrected water output value is obtained by multiplying the water output assessment value by the trend correction coefficient. Each set of water output value intervals corresponding to the corrected water output value is pre-constructed, and each set of water output value intervals corresponds to a set of opening adjustment ratios. The calculated corrected water output value is matched with each set of water output value intervals, and the resulting opening adjustment ratio is recorded as P2.

4. The method according to claim 3, wherein, The initial opening degree of the inlet gate of the drip irrigation head to obtain the next set time window is specifically as follows: The intake gate opening optimization ratio P3 is obtained by combining P1 and P2. If P3 is negative, it means that the intake gate opening is reduced; otherwise, it means that the intake gate opening is increased. The value of P3 is the specific reduction or increase ratio. The intake gate opening adjusted by the intake gate opening optimization ratio P3 is used as the initial intake gate opening for the next set time window.

5. The method according to claim 4, wherein, The analysis of filter operation data in the drip irrigation head unit operation data specifically includes: The pressure difference between the inlet and outlet of the filter is obtained at each time point within a set time window, and the average value is calculated to obtain the pressure difference evaluation value of the filter, which is represented by kt. The instantaneous flow of the water inlet and the instantaneous flow of the water outlet at each time point in a set time window are acquired, and the average values are calculated respectively to obtain the inlet average flow and the outlet average flow, which are represented by m1 and m2 respectively; the flow attenuation rate is calculated by the formula (m1-m2) / m1*100%, and is represented by kr; identify the duration of continuous operation of the filter since the last backwashing is completed, denoted as the continuous duration, denoted as kc; according to the formula calculate the filter hidden danger index of the filter within the set time window ; wherein respectively represent the differential pressure threshold value, the flow rate attenuation threshold rate, and the theoretical periodic backwashing duration; respectively represent the weight coefficients corresponding to the differential pressure evaluation value, the flow rate attenuation rate, and the continuous duration; compare the filter hidden danger index with the preset hidden danger threshold index, and if the filter hidden danger index is higher than the hidden danger threshold index, trigger the filter backwashing instruction.

6. The method according to claim 5, wherein, The crop data in the operation data of the drip irrigation head is analyzed, and specifically: The crop type and the growth stage of the current planted crop are extracted; the theoretical plant height and the theoretical leaf area of the current planted crop corresponding to different growth stages are preset; the plant height and the leaf area of e groups of the current planted crop are randomly collected, and the average values are calculated to obtain the actual plant height and the actual leaf area of the regional crop, where e>5; The actual plant height and the actual leaf area are taken as the numerators respectively, and the theoretical plant height and the theoretical leaf area are taken as the denominators respectively, and the ratio between each two is calculated to obtain the plant height performance value and the leaf performance value of the regional crop; the plant height performance value and the leaf performance value of the regional crop are multiplied by the preset plant height weight coefficient and the leaf weight coefficient respectively, and then the sum is obtained to obtain the growth evaluation value of the regional crop, which is denoted as Lr; The soil nutrient content of the irrigation area corresponding to the current regional crop is randomly collected, and the type number of the soil nutrient content is denoted by n, where n=1, 2, …, m, and m is the total number of the measured soil nutrient content types; the average value of each group of soil nutrient content of the same type is calculated to obtain the content evaluation value Gn of each type of soil nutrient of the current regional crop; presetting a theoretical soil nutrient requirement threshold value Vn corresponding to different growth stages of the current planted crop; calculating the nutrient content state value Lc of the current regional crop by using the formula calculating the nutrient content state value Lc of the current regional crop by using the formula representing the weight coefficient of the content evaluation value Gn corresponding to various types of soil nutrients.

7. The method according to claim 6, wherein, The fertilization plan of the drip irrigation head in the next set time window is obtained, and specifically: The growth evaluation value Lr of the regional crop is compared with the preset evaluation value normal interval, if the growth evaluation value Lr is higher than the evaluation value normal interval, it is determined that the regional crop is in the growth overgrowth state, if the growth evaluation value Lr is lower than the evaluation value normal interval, it is determined that the regional crop is in the growth lag state; if the growth evaluation value Lr is in the evaluation value normal interval, it is determined that the regional crop is in the growth normal state; The nutrient content state value Lc of the current regional crop is compared with the preset state value normal interval, if the nutrient content state value Lc is higher than the state value normal interval, it is determined that the regional crop is in the nutrient excess state, if the nutrient content state value Lc is lower than the state value normal interval, it is determined that the regional crop is in the nutrient lag state; if the nutrient content state value Lc is in the state value normal interval, it is determined that the regional crop is in the nutrient normal state; The growth overgrowth state, the growth lag state, the growth normal state, the nutrient excess state, the nutrient lag state and the nutrient normal state are marked as S1, S2, S3, U1, U2 and U3 respectively; If the current regional crop's state combination exhibits a fertilization reduction strategy is generated, wherein the fertilization reduction strategy includes a reduction in the number of fertilizations and a reduction in the proportional amount of fertilization; If the current regional crop's state combination exhibits a fertilization increase strategy is generated, wherein the fertilization decrease strategy includes an increase in the number of fertilizations and an increase in the amount of the proportion of the fertilizer.

8. The method according to claim 7, wherein, The fertilization plan of the drip irrigation head in the next set time window is obtained, and specifically: If the current region crop state combination exhibits an abnormal formulation strategy is generated; extracting each group of historical cases corresponding to the same state combination performance as the state combination performance of the abnormal decision strategy from the database; the historical state combination performance, the historical fertilization strategy, the fertilization crop type and the fertilization crop growth stage are included in each group of historical cases; the growth evaluation value and the nutrient content state value of each group of historical cases are extracted, and the formula is used to calculate the available confidence index ; wherein La and Lf represent the growth evaluation value and the nutrient content state value of each group of historical cases respectively, and b1 and b2 are the weight coefficients corresponding to the growth evaluation value Lr and the nutrient content state value Lc; the historical case with the lowest available confidence index is selected, and the historical fertilization strategy is extracted as the fertilization replacement strategy for the current regional crop. The generated fertilization reduction strategy, the fertilization increase strategy or the fertilization replacement strategy is taken as the fertilization plan of the current regional crop corresponding to the next set time window.

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